Gender identification of the horsehair crab, Erimacrus isenbeckii (Brandt, 1848), by image recognition with a deep neural network

Abstract

Abstract Appearance-based gender identification of the horsehair crab [Erimacrus isenbeckii (Brandt, 1848)] is important for preventing indiscriminate fishing of female crabs. Although their gender is easily identified by visual observation of their abdomen because of a difference in the forms of their sex organs, most of the crabs settle with their shell side upward when placed on a floor, making visual gender identification difficult. Our objective is to use deep learning to identify the gender of the horsehair crab on the basis of images of their shell and abdomen sides. Deep learning was applied to a photograph of 60 males and 60 females captured in Funka Bay, Southern Hokkaido, Japan. The deep learning algorithms used the AlexNet, VGG-16, and ResNet-50 convolutional neural networks. The VGG-16 network achieved high accuracy. Heatmaps were enhanced near the forms of the sex organs in the abdomen side (F-1 measure: 98%). The bottom of the shell was enhanced in the heatmap of a male; by contrast, the upper part of the shell was enhanced in the heatmap of a female (F-1 measure: 95%). The image recognition of the shell side based on a deep learning algorithm enabled more precise gender identification than could be achieved by human-eye inspection.